Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa.
Spatiotemporal Analysis of the 2014 Ebola Epidemic in West Africa.
复制标题
DOI：
10.1371/journal.pcbi.1005210
复制
发表时间：
2016-12
影响因子：
4.3
通讯作者：
Wallinga J
中科院分区：
文献类型：
--
作者：
Backer JA;Wallinga J
2014 - 2016年，西非的几内亚、塞拉利昂和利比里亚经历了自1976年发现该病毒以来规模最大、持续时间最长的埃博拉疫情。在流行病期间，收集和公布发病率数据的分辨率越来越高。为了监测流行病，因为它在区域内和区域之间传播，我们开发了一种分析方法，利用充分的时空分辨率的数据相结合的局部模型随时间变化的有效繁殖数量与重力型模型的空间分散的感染。我们在模拟中测试了这种方法，并将其应用于世界卫生组织报告的截至2015年6月每个地区的确诊和可能病例的每周发病率。我们的研究结果表明，在新感染的病例中，只有一小部分（4%至10%）迁移到另一个地区，这些移民中的少数（0%至23%）离开了他们的国家。在这三个国家的流行病被认为是相似的估计有效繁殖数量，并在输入感染到一个地区的概率。这些国家可能在跨境传播中发挥了不同的作用，但敏感性分析表明，这也可能与报告不足有关。时空分析方法可以利用不同地理位置的可用纵向发病率数据来监测当地流行病，确定空间传播的程度，揭示当地和输入病例的贡献，并确定未感染地区的传入来源。有了高质量的发病率数据，这种数据驱动的方法可以帮助有效地控制新出现的感染。传染病建模已成为一个既定的工具，为感染控制决策提供信息。对于局限于一个地理位置的疾病暴发，有建模方法来分析流行病数据、监测感染发生率和评估控制措施的效果。对于蔓延到各个邻近地区或国家的疫情，例如最近在西非发生的埃博拉疫情，几乎没有可以分析时空数据的建模方法。为了研究地区和国家内部和之间的流行病传播，我们开发了一种分析方法，使用这些数据的全分辨率。疫情表现为一个地方流行病网络，通过在一个地区感染但在另一个地区被观察到感染的旅行者相互联系。这种方法的主要优点是，它需要的数据很少，不对参数值作强有力的假设;缺点是它没有考虑到漏报。时空方法可以监测当地流行病的发展，确定空间传播的程度，揭示本地和输入性病例的贡献，并确定未感染地区的传入来源。这些结果可以帮助有效地控制新出现的感染。
In 2014–2016, Guinea, Sierra Leone and Liberia in West Africa experienced the largest and longest Ebola epidemic since the discovery of the virus in 1976. During the epidemic, incidence data were collected and published at increasing resolution. To monitor the epidemic as it spread within and between districts, we develop an analysis method that exploits the full spatiotemporal resolution of the data by combining a local model for time-varying effective reproduction numbers with a gravity-type model for spatial dispersion of the infection. We test this method in simulations and apply it to the weekly incidences of confirmed and probable cases per district up to June 2015, as reported by the World Health Organization. Our results indicate that, of the newly infected cases, only a small percentage, between 4% and 10%, migrates to another district, and a minority of these migrants, between 0% and 23%, leave their country. The epidemics in the three countries are found to be similar in estimated effective reproduction numbers, and in the probability of importing infection into a district. The countries might have played different roles in cross-border transmissions, although a sensitivity analysis suggests that this could also be related to underreporting. The spatiotemporal analysis method can exploit available longitudinal incidence data at different geographical locations to monitor local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. With good quality data on incidence, this data-driven method can help to effectively control emerging infections. Infectious disease modelling has become an established tool to inform decisions in infection control. For outbreaks that are confined to one geographical location, modelling approaches exist to analyse epidemic data, monitor infection incidence and assess the effect of control measures. For outbreaks that are spread over various adjacent districts or countries, such as the recent Ebola epidemic in West Africa, few modelling approaches are available that can analyse the spatiotemporal data. To study the epidemic spread within and between districts and countries, we have developed an analysis method that uses the full resolution of these data. The outbreak is represented as a network of local epidemics that are interconnected through travellers that are infected in one district, but observed to be infected in another. The main advantages of this method are that it needs little data and does not make strong assumptions on parameter values; a disadvantage is that it does not take underreporting into account. The spatiotemporal method can monitor the development of local epidemics, determine the extent of spatial spread, reveal the contribution of local and imported cases, and identify sources of introductions in uninfected areas. These results can help to effectively control emerging infections.
登录
查看更多内容
影响因子：
56.3
作者：
Cauchemez, Simon;Fraser, Christophe;Van Kerkhove, Maria D.;Donnelly, Christi A.;Riley, Steven;Rambaut, Andrew;Enouf, Vincent;van der Werf, Sylvie;Ferguson, Neil M.
通讯作者：
Ferguson, Neil M.
影响因子：
19
作者：
Nishiura, H.;Chowell, G.
通讯作者：
Chowell, G.
DOI：
10.1056/nejmoa1411100
发表时间：
2014-10-16
期刊：
The New England journal of medicine
影响因子：
--
作者：
WHO Ebola Response Team;Aylward B;Barboza P;Bawo L;Bertherat E;Bilivogui P;Blake I;Brennan R;Briand S;Chakauya JM;Chitala K;Conteh RM;Cori A;Croisier A;Dangou JM;Diallo B;Donnelly CA;Dye C;Eckmanns T;Ferguson NM;Formenty P;Fuhrer C;Fukuda K;Garske T;Gasasira A;Gbanyan S;Graaff P;Heleze E;Jambai A;Jombart T;Kasolo F;Kadiobo AM;Keita S;Kertesz D;Koné M;Lane C;Markoff J;Massaquoi M;Mills H;Mulba JM;Musa E;Myhre J;Nasidi A;Nilles E;Nouvellet P;Nshimirimana D;Nuttall I;Nyenswah T;Olu O;Pendergast S;Perea W;Polonsky J;Riley S;Ronveaux O;Sakoba K;Santhana Gopala Krishnan R;Senga M;Shuaib F;Van Kerkhove MD;Vaz R;Wijekoon Kannangarage N;Yoti Z
通讯作者：
Yoti Z
DOI：
10.1126/science.1259657
发表时间：
2014-09-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gire SK;Goba A;Andersen KG;Sealfon RS;Park DJ;Kanneh L;Jalloh S;Momoh M;Fullah M;Dudas G;Wohl S;Moses LM;Yozwiak NL;Winnicki S;Matranga CB;Malboeuf CM;Qu J;Gladden AD;Schaffner SF;Yang X;Jiang PP;Nekoui M;Colubri A;Coomber MR;Fonnie M;Moigboi A;Gbakie M;Kamara FK;Tucker V;Konuwa E;Saffa S;Sellu J;Jalloh AA;Kovoma A;Koninga J;Mustapha I;Kargbo K;Foday M;Yillah M;Kanneh F;Robert W;Massally JL;Chapman SB;Bochicchio J;Murphy C;Nusbaum C;Young S;Birren BW;Grant DS;Scheiffelin JS;Lander ES;Happi C;Gevao SM;Gnirke A;Rambaut A;Garry RF;Khan SH;Sabeti PC
通讯作者：
Sabeti PC
影响因子：
5
作者：
Wallinga, J;Teunis, P
通讯作者：
Teunis, P